commit

app.py

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+from langchain_community.vectorstores import Qdrant
+from langchain_together import Together
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from qdrant_client import QdrantClient
+from langchain_core.prompts import PromptTemplate
+import os
+from dotenv import load_dotenv
+
+from langchain_community.vectorstores import Qdrant
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.docstore.document import Document
+import pandas as pd
+
+# formatting the data for ingestion
+all_prods_df = pd.read_csv("data/cleaned_CSVIndian10000.csv")
+all_prods_df = all_prods_df.fillna("")
+
+product_metadata = all_prods_df.to_dict(orient="index")
+
+texts = [str(v['name']) + "\n" + str(v['product_desc']) for k, v in product_metadata.items()]
+
+metadatas = list(product_metadata.values())
+
+docs = [Document(page_content=txt, metadata={"source": meta}) for txt, meta in zip(texts, metadatas)]
+
+print("Data loaded.........")
+
+
+# load the embedding model
+model_name = "BAAI/bge-large-en"
+model_kwargs = {"device": "cpu"}
+encode_kwargs = {"normalize_embeddings": True}
+
+embeddings = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+
+print("Embedding model loaded.........")
+
+
+# load the vector store
+# url="http://localhost:6333"
+collection_name = "shopintel100v3"
+
+vector_store = Qdrant.from_documents(
+    docs,
+    embeddings,
+    location=":memory:",
+    collection_name=collection_name,
+    prefer_grpc = False
+)
+
+print("Vector store loaded.........")
+
+
+load_dotenv()
+
+TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
+print("api key: ", TOGETHER_API_KEY, type(TOGETHER_API_KEY))
+
+
+# load the embedding model
+# model_name = "BAAI/bge-large-en"
+# model_kwargs = {"device": "cpu"}
+# encode_kwargs = {"normalize_embeddings": True}
+
+# embeddings = HuggingFaceBgeEmbeddings(
+#     model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
+# )
+# print("embeddings loaded.............")
+
+# url = "http://localhost:6333"
+# collection_name = "shopintel100v3"
+
+# client = QdrantClient(url=url, prefer_grpc=False)
+
+# vector_store = Qdrant(
+#     client=client, 
+#     collection_name=collection_name, 
+#     embeddings=embeddings
+# )
+
+print("qdrant embeddings from docker were loaded.............")
+
+llm = Together(
+    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    temperature=0.2,
+    max_tokens=5000,
+    top_k=50,
+    together_api_key=TOGETHER_API_KEY
+)
+
+
+# query = "ASUS VivoBook 15 (2021)"
+# result = vector_store.similarity_search_with_score(query=query, k=5)
+
+# for i in result:
+#     doc, score = i
+#     print({"score": score, "content": doc.page_content, "metadata": doc.metadata["source"]})
+#     print("---------------------------------")
+
+# function to retrieve products from qdrant
+
+def retrieve_product(user_input, vector_store, k = 10):
+    result = vector_store.similarity_search_with_score(
+      query=user_input,
+      k=k
+    )
+
+    return result
+
+
+# function to create context from user query
+
+def create_context(user_input, vector_store):
+    result = retrieve_product(user_input, vector_store)
+
+    context = ""
+    for index, value in enumerate(result):
+        product = value
+        product_title = product[0].page_content # Extracting the page_content for each result which is a string
+        product_metadata = product[0].metadata["source"] # Extracting the metadata for each result which is a dictionary with key values
+
+        context += f"""
+        * Product {index + 1} -
+          - Product name : {product_metadata["name"]}
+          - Product price: {product_metadata["discount_price"]}
+          - Brief description of the product: {product_metadata["product_desc"]}
+          - Detailed description of the product: {product_metadata["about_this_item"]}
+          - Rating value (1.0 - 5.0): {product_metadata["ratings"]}
+          - Overall review: {product_metadata["overall_review"]}
+
+
+        """
+        # print(f"product_title: {type(product_title)}", product_title)
+        # print(f"product_metadata: {type(product_metadata)}", product_metadata)
+
+    return context
+
+
+
+# prompt template for the mistral model
+
+template = """You are a friendly, conversational AI ecommerce assistant. The context includes 5 ecommerce products.
+Use only the following context, to find the answer to the questions from the customer.
+
+Its very important that you follow the below instructions.
+ -Dont use general knowledge to answer the question
+ -If you dont find the answer from the context or the question is not related to the context, just say that you don't know the answer.
+ -By any chance the customer should not know you are referring to a context. 
+
+
+Context:
+
+{context}
+
+
+Question:
+{question}
+
+
+Helpful Answer:"""
+
+
+import random
+import gradio as gr
+
+chat_history = []
+def respond(message, chat_history):
+    global  vector_store, template, llm
+    chatbot_response = ""
+    try:
+        context = create_context(message, vector_store)
+        print("context:-------------------------\n", context)
+        prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+        prompt_formatted_str = prompt.format(
+            context=context,
+            question=message
+        )
+        output = llm.invoke(prompt_formatted_str)
+        chat_history.append((message, output))
+        return "", chat_history
+    except Exception as e:
+        print("Error:", e)
+        error_responses = [
+            "Sorry, I encountered an error while processing your request.",
+            "Oops, something went wrong. Please try again later.",
+            "I'm having trouble understanding that. Can you please rephrase?",
+            "It seems there was an issue. Let's try something else."
+        ]
+        error_message = random.choice(error_responses)
+        output = error_message
+        chat_history.append((message, output))
+        return "", chat_history
+
+# Define the Gradio interface
+# chatbot = gr.Chatbot(height=450)
+# msg = gr.Textbox(label="What would you like to know?")
+# gr.Interface(
+#     fn=respond,
+#     inputs=msg,
+#     outputs=gr.Textbox(label="Response"),
+#     title="Conversational AI Chatbot",
+# ).launch(
+#     share=True,
+# )
+
+# # Define Gradio components
+with gr.Blocks() as demo:
+    chat_history = []
+    chatbot = gr.Chatbot(height=450)
+    msg = gr.Textbox(label="What would you like to know?")
+    btn = gr.Button("Submit")
+    clear = gr.ClearButton(value="Clear Console", components=[msg, chatbot])
+
+    # Button click event to respond to the message
+    btn.click(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+
+    # Clear button event to clear the console
+    msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+
+# Define the Gradio interface
+gr.close_all()
+
+demo.launch()